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eynollah_ocr: actually replace the model calls

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kba 2025-10-22 10:40:49 +02:00
parent 1337461d47
commit 4c8abfe19c
1 changed files with 10 additions and 10 deletions
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src/eynollah/eynollah_ocr.py
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@ -199,7 +199,7 @@ class Eynollah_ocr:
indexer_b_s = 0 indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_ocr.generate( generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device)) pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_text_merged = self.model_zoo.get('processor').batch_decode(
generated_ids_merged, skip_special_tokens=True) generated_ids_merged, skip_special_tokens=True)
@ -222,7 +222,7 @@ class Eynollah_ocr:
indexer_b_s = 0 indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_ocr.generate( generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device)) pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_text_merged = self.model_zoo.get('processor').batch_decode(
generated_ids_merged, skip_special_tokens=True) generated_ids_merged, skip_special_tokens=True)
@ -242,7 +242,7 @@ class Eynollah_ocr:
indexer_b_s = 0 indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_ocr.generate( generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device)) pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_text_merged = self.model_zoo.get('processor').batch_decode(
generated_ids_merged, skip_special_tokens=True) generated_ids_merged, skip_special_tokens=True)
@ -260,7 +260,7 @@ class Eynollah_ocr:
indexer_b_s = 0 indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_ocr.generate( generated_ids_merged = self.model_zoo.get('ocr').generate(
pixel_values_merged.to(self.device)) pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_text_merged = self.model_zoo.get('processor').batch_decode(
generated_ids_merged, skip_special_tokens=True) generated_ids_merged, skip_special_tokens=True)
@ -277,7 +277,7 @@ class Eynollah_ocr:
indexer_b_s = 0 indexer_b_s = 0
pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values
generated_ids_merged = self.model_ocr.generate(pixel_values_merged.to(self.device)) generated_ids_merged = self.model_zoo.get('ocr').generate(pixel_values_merged.to(self.device))
generated_text_merged = self.model_zoo.get('processor').batch_decode(generated_ids_merged, skip_special_tokens=True) generated_text_merged = self.model_zoo.get('processor').batch_decode(generated_ids_merged, skip_special_tokens=True)
extracted_texts = extracted_texts + generated_text_merged extracted_texts = extracted_texts + generated_text_merged
@ -753,10 +753,10 @@ class Eynollah_ocr:
self.logger.debug("processing next %d lines", len(imgs)) self.logger.debug("processing next %d lines", len(imgs))
preds = self.prediction_model.predict(imgs, verbose=0) preds = self.model_zoo.get('ocr').predict(imgs, verbose=0)
if len(indices_ver)>0: if len(indices_ver)>0:
preds_flipped = self.prediction_model.predict(imgs_ver_flipped, verbose=0) preds_flipped = self.model_zoo.get('ocr').predict(imgs_ver_flipped, verbose=0)
preds_max_fliped = np.max(preds_flipped, axis=2 ) preds_max_fliped = np.max(preds_flipped, axis=2 )
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 ) preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
@ -786,10 +786,10 @@ class Eynollah_ocr:
preds[indices_to_be_replaced,:,:] = \ preds[indices_to_be_replaced,:,:] = \
preds_flipped[indices_where_flipped_conf_value_is_higher, :, :] preds_flipped[indices_where_flipped_conf_value_is_higher, :, :]
if dir_in_bin is not None: if dir_in_bin is not None:
preds_bin = self.prediction_model.predict(imgs_bin, verbose=0) preds_bin = self.model_zoo.get('ocr').predict(imgs_bin, verbose=0)
if len(indices_ver)>0: if len(indices_ver)>0:
preds_flipped = self.prediction_model.predict(imgs_bin_ver_flipped, verbose=0) preds_flipped = self.model_zoo.get('ocr').predict(imgs_bin_ver_flipped, verbose=0)
preds_max_fliped = np.max(preds_flipped, axis=2 ) preds_max_fliped = np.max(preds_flipped, axis=2 )
preds_max_args_flipped = np.argmax(preds_flipped, axis=2 ) preds_max_args_flipped = np.argmax(preds_flipped, axis=2 )
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
@ -821,7 +821,7 @@ class Eynollah_ocr:
preds = (preds + preds_bin) / 2. preds = (preds + preds_bin) / 2.
pred_texts = decode_batch_predictions(preds, self.num_to_char) pred_texts = decode_batch_predictions(preds, self.model_zoo.get('num_to_char'))
preds_max = np.max(preds, axis=2 ) preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 ) preds_max_args = np.argmax(preds, axis=2 )